Project Overview
AI-powered meeting analysis platform that converts meeting audio into transcripts, summaries, action items, key discussion points, sentiment insights, and topic extraction using Whisper, NLP, and FastAPI.
AI / NLP / Speech Processing • Completed
Meeting Notes AI
AI-powered meeting analysis platform that converts meeting audio into transcripts, summaries, action items, key discussion points, sentiment insights, and topic extraction using Whisper, NLP, and FastAPI.
The Problem
Organizations spend significant time manually reviewing meeting recordings and preparing notes. Important decisions, action items, and discussion points can be missed, reducing productivity and collaboration. Existing manual documentation processes are time-consuming, inconsistent, and difficult to scale for frequent meetings.
The Solution
Developed an AI-powered meeting intelligence system that automatically transcribes meeting audio and generates structured insights. The platform produces meeting summaries, extracts key points, identifies action items, analyzes sentiment, and detects discussion topics using speech-to-text and natural language processing techniques. The system supports both fully local AI processing and OpenAI-powered cloud processing modes.
Architecture & System Flow
1. User uploads a meeting audio file through the web interface. 2. FastAPI backend receives and validates the audio file. 3. Whisper converts speech into text transcripts. 4. NLP services process the transcript. 5. Transformer models generate concise meeting summaries. 6. Key discussion points are extracted. 7. Action items and follow-up tasks are identified. 8. Sentiment analysis evaluates meeting tone and engagement. 9. Topic extraction identifies major discussion themes. 10. Results are returned through REST APIs and displayed in the Flask frontend.
Key Features
- ✔ Speech-to-text transcription
- ✔ Meeting summarization
- ✔ Key point extraction
- ✔ Action item detection
- ✔ Sentiment analysis
- ✔ Topic extraction
- ✔ FastAPI REST API
- ✔ Flask web interface
- ✔ Local AI processing mode
- ✔ OpenAI processing mode
- ✔ Large audio file support
- ✔ Multi-format audio upload
- ✔ NLP-based meeting insights
- ✔ Scalable backend architecture
- ✔ Environment-based configuration
Challenges Faced
Challenge 1: Processing long meeting recordings efficiently. Solution: Implemented support for large file uploads and optimized transcription workflows. Challenge 2: Generating meaningful summaries from lengthy transcripts. Solution: Integrated Hugging Face Transformer models and OpenAI summarization capabilities. Challenge 3: Supporting users without paid AI APIs. Solution: Designed a fully local AI pipeline using Whisper, Transformers, NLTK, and TextBlob. Challenge 4: Extracting actionable insights from unstructured conversations. Solution: Combined NLP techniques including tokenization, sentiment analysis, keyword extraction, and topic modeling. Challenge 5: Managing multiple AI processing workflows. Solution: Created configurable processing modes allowing seamless switching between Local and OpenAI services.
Results & Metrics
• Supports audio files up to 200 MB • Automatic speech-to-text transcription • Meeting summarization using transformer models • Action item extraction and follow-up tracking • Sentiment and tone analysis • Topic extraction using TF-IDF • REST API support for external integrations • Dual processing modes (Local AI and OpenAI) • End-to-end meeting intelligence workflow automation
Project Gallery
Tech Stack
Python
FastAPI
Flask
OpenAI Whisper
Hugging Face Transformers
PyTorch
NLTK
TextBlob
Scikit-Learn
HTML
CSS
JavaScript
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